In retail, SI earns its keep in conversion, basket size, and margin. We build personalisation, semantic search, and demand forecasting that lift revenue per visitor and take cost out of fulfilment and support, measured against your numbers, not a benchmark.

Get a production-readiness review What makes e-commerce SI hard

01 · Use cases

Six ways we deploy SI in e-commerce.

From semantic search to demand forecasting, every engagement is scoped around your catalogue, your data, and the revenue levers that matter most.

01 · Use case

Semantic Product Search

Vector search over your catalogue. Customers find what they mean, not just what they typed. Handles synonyms, misspellings, and intent.

02 · Use case

Personalisation Engine

Real-time product recommendations based on browsing, purchase history, and contextual signals. Not collaborative filtering from 2012.

03 · Use case

SI Customer Service

LLM-powered support agents trained on your FAQs, policies, and product data. Handle returns, tracking, and product questions without a human.

04 · Use case

Demand & Inventory Forecasting

ML models that predict sell-through rates, seasonal demand, and stockout risk. Reduce overstock and lost sales simultaneously.

05 · Use case

Dynamic Pricing

Pricing models that respond to competitor prices, demand signals, and inventory levels in real time. With guardrails to protect margin floors.

06 · Use case

Review & Sentiment Intelligence

NLP pipelines over product reviews and support tickets. Surface quality issues, competitive intelligence, and NPS drivers automatically.

02 · What makes e-commerce SI hard

Four challenges we solve first.

Catalogue scale breaks rule-based systems
Thousands of SKUs, attributes, and categories require ML, not if-else logic. We build for catalogue scale from day one, not as a retrofit.
Relevance is not recall
Returning 1,000 results is not helpful. SI search needs to understand intent and rank by business value, not just keyword match.
Personalisation without creepiness
Users want relevant suggestions, not surveillance. We design personalisation with privacy-first data practices built into the architecture.
Cold-start for new products
New SKUs have no history. Content-based embeddings and category priors handle cold-start without waiting for data to accumulate.

Proof, not promises

Twenty-plus SI systems shipped to production. One playbook, six industries, and a team that stays past launch.

SI systems live in production: 20+
Senior engineers & researchers: 21+
Upwork rating, 3 verified reviews: 5.0
Founded in Lahore: 2022

Verified Upwork clientSI product engagement · 5-star review

03 · Common questions

Before you brief us.

How quickly can you improve our search relevance?: A semantic search upgrade typically takes 4–6 weeks: data audit, embedding model selection, index build, and A/B test setup. Most clients see measurable conversion improvement within the first test cycle.
Can you integrate with our existing e-commerce platform?: Yes. We've built SI layers on top of Shopify, Magento, WooCommerce, and custom platforms. We expose SI features via API so your storefront doesn't need a full rebuild.
How do you handle a catalogue of 100k+ SKUs?: At that scale we typically use a two-stage retrieval pipeline: fast approximate nearest-neighbour search to retrieve candidates, followed by a reranking model that applies business rules and personalisation signals.
What does an SI customer service agent actually handle?: Order status, returns and refunds, product questions, and policy lookups: the queries that make up 70–80% of ticket volume for most retailers. Escalations route to human agents with full context.

Available for new engagements

Book a free e-commerce SI scoping call.

30 minutes, one of our seniors, no slide deck. By the end of the call you will know whether we are the right team, and if not, who is.

Get a production-readiness review Email info@octalcode.com

Or call +1 (512) 710-5701

Let's scope it.Instant answers · free project scoping